Easy to use Audio Tagging in PyTorch

Related tags

Deep Learningaudiopytorchaudio-classificationsound-event-detectionaudio-taggingcnn14
Overview

Audio Classification, Tagging & Sound Event Detection in PyTorch

Progress:

  • Fine-tune on audio classification
  • Fine-tune on audio tagging
  • Fine-tune on sound event detection
  • Add tagging metrics
  • Add Tutorial
  • Add Augmentation Notebook
  • Add more schedulers
  • Add FSDKaggle2019 dataset
  • Add MTT dataset
  • Add DESED

Model Zoo

AudioSet Pretrained Models
Model Task mAP
(%)		 Sample Rate
(kHz)		 Window Length Num Mels Fmax Weights
CNN14 Tagging 43.1 32 1024 64 14k download
CNN14_16k Tagging 43.8 16 512 64 8k download
CNN14_DecisionLevelMax SED 38.5 32 1024 64 14k download

Note: These models will be used as a pretrained model in the fine-tuning tasks below. Check out audioset-tagging-cnn, if you want to train on AudioSet dataset.

Fine-tuned Classification Models
Model Dataset Accuracy
(%)		 Sample Rate
(kHz)		 Weights
CNN14 ESC50 (Fold-5) 95.75 32 download
CNN14 FSDKaggle2018 (test) 93.56 32 download
CNN14 SpeechCommandsv1 (val/test) 96.60/96.77 32 download
Fine-tuned Tagging Models
Model Dataset mAP(%) AUC d-prime Sample Rate
(kHz)		 Config Weights
CNN14 FSDKaggle2019 - - - 32 - -
Fine-tuned SED Models
Model Dataset F1 Sample Rate
(kHz)		 Config Weights
CNN14_DecisionLevelMax DESED - 32 - -

Supported Datasets

Dataset Task Classes Train Val Test Audio Length Audio Spec Size
ESC-50 Classification 50 2,000 5 folds - 5s 44.1kHz, mono 600MB
UrbanSound8k Classification 10 8,732 10 folds - <=4s Vary 5.6GB
FSDKaggle2018 Classification 41 9,473 - 1,600 300ms~30s 44.1kHz, mono 4.6GB
SpeechCommandsv1 Classification 30 51,088 6,798 6,835 <=1s 16kHz, mono 1.4GB
SpeechCommandsv2 Classification 35 84,843 9,981 11,005 <=1s 16kHz, mono 2.3GB
FSDKaggle2019* Tagging 80 4,970+19,815 - 4,481 300ms~30s 44.1kHz, mono 24GB
MTT* Tagging 50 19,000 - - - - 3GB
DESED* SED 10 - - - 10 - -

Notes: * datasets are not available yet. Classification dataset are treated as multi-class/single-label classification and tagging and sed datasets are treated as multi-label classification.

Dataset Structure (click to expand)

Download the dataset and prepare it into the following structure.

datasets
|__ ESC50
    |__ audio

|__ Urbansound8k
    |__ audio

|__ FSDKaggle2018
    |__ audio_train
    |__ audio_test
    |__ FSDKaggle2018.meta
        |__ train_post_competition.csv
        |__ test_post_competition_scoring_clips.csv

|__ SpeechCommandsv1/v2
    |__ bed
    |__ bird
    |__ ...
    |__ testing_list.txt
    |__ validation_list.txt

Augmentations (click to expand)

Currently, the following augmentations are supported. More will be added in the future. You can test the effects of augmentations with this notebook

WaveForm Augmentations:

  • MixUp
  • Background Noise
  • Gaussian Noise
  • Fade In/Out
  • Volume
  • CutMix

Spectrogram Augmentations:

  • Time Masking
  • Frequency Masking
  • Filter Augmentation

Usage

Requirements (click to expand)
  • python >= 3.6
  • pytorch >= 1.8.1
  • torchaudio >= 0.8.1

Other requirements can be installed with pip install -r requirements.txt.


Configuration (click to expand)
  • Create a configuration file in configs. Sample configuration for ESC50 dataset can be found here.
  • Copy the contents of this and then edit the fields you think if it is needed.
  • This configuration file is needed for all of training, evaluation and prediction scripts.

Training (click to expand)

To train with a single GPU:

$ python tools/train.py --cfg configs/CONFIG_FILE_NAME.yaml

To train with multiple gpus, set DDP field in config file to true and run as follows:

$ python -m torch.distributed.launch --nproc_per_node=2 --use_env tools/train.py --cfg configs/CONFIG_FILE_NAME.yaml

Evaluation (click to expand)

Make sure to set MODEL_PATH of the configuration file to your trained model directory.

$ python tools/val.py --cfg configs/CONFIG_FILE.yaml

Audio Classification/Tagging Inference
  • Set MODEL_PATH of the configuration file to your model's trained weights.
  • Change the dataset name in DATASET >> NAME as your trained model's dataset.
  • Set the testing audio file path in TEST >> FILE.
  • Run the following command.
$ python tools/infer.py --cfg configs/CONFIG_FILE.yaml

## for example
$ python tools/infer.py --cfg configs/audioset.yaml

You will get an output similar to this:

Class                     Confidence
----------------------  ------------
Speech                     0.897762
Telephone bell ringing     0.752206
Telephone                  0.219329
Inside, small room         0.20761
Music                      0.0770325

Sound Event Detection Inference
  • Set MODEL_PATH of the configuration file to your model's trained weights.
  • Change the dataset name in DATASET >> NAME as your trained model's dataset.
  • Set the testing audio file path in TEST >> FILE.
  • Run the following command.
$ python tools/sed_infer.py --cfg configs/CONFIG_FILE.yaml

## for example
$ python tools/sed_infer.py --cfg configs/audioset_sed.yaml

You will get an output similar to this:

Class                     Start    End
----------------------  -------  -----
Speech                      2.2    7
Telephone bell ringing      0      2.5

The following plot will also be shown, if you set PLOT to true:

sed_result


References (click to expand)

Citations (click to expand) 
@misc{kong2020panns,
      title={PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition}, 
      author={Qiuqiang Kong and Yin Cao and Turab Iqbal and Yuxuan Wang and Wenwu Wang and Mark D. Plumbley},
      year={2020},
      eprint={1912.10211},
      archivePrefix={arXiv},
      primaryClass={cs.SD}
}

@misc{gong2021ast,
      title={AST: Audio Spectrogram Transformer}, 
      author={Yuan Gong and Yu-An Chung and James Glass},
      year={2021},
      eprint={2104.01778},
      archivePrefix={arXiv},
      primaryClass={cs.SD}
}

@misc{nam2021heavily,
      title={Heavily Augmented Sound Event Detection utilizing Weak Predictions}, 
      author={Hyeonuk Nam and Byeong-Yun Ko and Gyeong-Tae Lee and Seong-Hu Kim and Won-Ho Jung and Sang-Min Choi and Yong-Hwa Park},
      year={2021},
      eprint={2107.03649},
      archivePrefix={arXiv},
      primaryClass={eess.AS}
}
You might also like...
TorchMetrics is a collection of 25+ PyTorch metrics implementations and an easy-to-use API to create custom metrics.
TorchMetrics is a collection of 25+ PyTorch metrics implementations and an easy-to-use API to create custom metrics.

Machine learning metrics for distributed, scalable PyTorch applications.

Pytorch Lightning 1.2k Jan 6, 2023
TorchFlare is a simple, beginner-friendly, and easy-to-use PyTorch Framework train your models effortlessly.
TorchFlare is a simple, beginner-friendly, and easy-to-use PyTorch Framework train your models effortlessly.

TorchFlare TorchFlare is a simple, beginner-friendly and an easy-to-use PyTorch Framework train your models without much effort. It provides an almost

Atharva Phatak 85 Dec 26, 2022
A more easy-to-use implementation of KPConv based on PyTorch.

A more easy-to-use implementation of KPConv This repo contains a more easy-to-use implementation of KPConv based on PyTorch. Introduction KPConv is a

Zheng Qin 36 Dec 29, 2022
Use MATLAB to simulate the signal and extract features. Use PyTorch to build and train deep network to do spectrum sensing.

Deep-Learning-based-Spectrum-Sensing Use MATLAB to simulate the signal and extract features. Use PyTorch to build and train deep network to do spectru

null 10 Dec 14, 2022
Fast image augmentation library and easy to use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about library: https://www.mdpi.com/2078-2489/11/2/125
Fast image augmentation library and easy to use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about library: https://www.mdpi.com/2078-2489/11/2/125

Albumentations Albumentations is a Python library for image augmentation. Image augmentation is used in deep learning and computer vision tasks to inc

null 11.4k Jan 9, 2023
Fast, flexible and easy to use probabilistic modelling in Python.
Fast, flexible and easy to use probabilistic modelling in Python.

Please consider citing the JMLR-MLOSS Manuscript if you've used pomegranate in your academic work! pomegranate is a package for building probabilistic

Jacob Schreiber 3k Dec 29, 2022
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

What is xLearn? xLearn is a high performance, easy-to-use, and scalable machine learning package that contains linear model (LR), factorization machin

Chao Ma 3k Jan 3, 2023
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

What is xLearn? xLearn is a high performance, easy-to-use, and scalable machine learning package that contains linear model (LR), factorization machin

Chao Ma 2.8k Feb 12, 2021
A fast and easy to use, moddable, Python based Minecraft server!
A fast and easy to use, moddable, Python based Minecraft server!

PyMine PyMine - The fastest, easiest to use, Python-based Minecraft Server! Features Note: This list is not always up to date, and doesn't contain all

PyMine 144 Dec 30, 2022
Releases(v0.2.0)

  • v0.2.0(Aug 17, 2021)

    This release includes the following:

    • Fine-tuned on ESC50, FSDKaggle2018, SpeechCommandsv1
    • Add waveform augmentations
    • Add spectrogram augmentations
    • Add augmentation testing notebook
    • Add tagging metrics
    Source code(tar.gz)
    Source code(zip)

  • v0.1.0(Aug 13, 2021)

Owner
sithu3
AI Developer
sithu3
GitHub Repository
Official Implementation of DE-DETR and DELA-DETR in "Towards Data-Efficient Detection Transformers"

DE-DETRs By Wen Wang, Jing Zhang, Yang Cao, Yongliang Shen, and Dacheng Tao This repository is an official implementation of DE-DETR and DELA-DETR in

Wen Wang 61 Dec 12, 2022
Code of paper "CDFI: Compression-Driven Network Design for Frame Interpolation", CVPR 2021

CDFI (Compression-Driven-Frame-Interpolation) [Paper] (Coming soon...) | [arXiv] Tianyu Ding*, Luming Liang*, Zhihui Zhu, Ilya Zharkov IEEE Conference

Tianyu Ding 95 Dec 04, 2022
Yolov5 + Deep Sort with PyTorch

딥소트 수정중 Yolov5 + Deep Sort with PyTorch Introduction This repository contains a two-stage-tracker. The detections generated by YOLOv5, a family of obj

1 Nov 26, 2021
Ganilla - Official Pytorch implementation of GANILLA

GANILLA We provide PyTorch implementation for: GANILLA: Generative Adversarial Networks for Image to Illustration Translation. Paper Arxiv Updates (Fe

Samet Hi 462 Dec 05, 2022
A Python library that provides a simplified alternative to DBAPI 2

A Python library that provides a simplified alternative to DBAPI 2. It provides a facade in front of DBAPI 2 drivers.

Tony Locke 44 Nov 17, 2021
A PyTorch implementation of " EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks."

EfficientNet A PyTorch implementation of EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. [arxiv] [Official TF Repo] Implemen

AhnDW 298 Dec 10, 2022
This tool uses Deep Learning to help you draw and write with your hand and webcam.

This tool uses Deep Learning to help you draw and write with your hand and webcam. A Deep Learning model is used to try to predict whether you want to have 'pencil up' or 'pencil down'.

lmagne 169 Dec 10, 2022
Automatic 2D-to-3D Video Conversion with CNNs

Deep3D: Automatic 2D-to-3D Video Conversion with CNNs How To Run To run this code. Please install MXNet following the official document. Deep3D requir

Eric Junyuan Xie 1.2k Dec 30, 2022
[CVPR 2022] Deep Equilibrium Optical Flow Estimation

Deep Equilibrium Optical Flow Estimation This is the official repo for the paper Deep Equilibrium Optical Flow Estimation (CVPR 2022), by Shaojie Bai*

CMU Locus Lab 136 Dec 18, 2022
HGCN: Harmonic Gated Compensation Network For Speech Enhancement

HGCN The official repo of "HGCN: Harmonic Gated Compensation Network For Speech Enhancement", which was accepted at ICASSP2022. How to use step1: Calc

ScorpioMiku 33 Nov 14, 2022
Graph WaveNet apdapted for brain connectivity analysis.

Graph WaveNet for brain network analysis This is the implementation of the Graph WaveNet model used in our manuscript: S. Wein , A. Schüller, A. M. To

4 Dec 17, 2022
This repository contains pre-trained models and some evaluation code for our paper Towards Unsupervised Dense Information Retrieval with Contrastive Learning

Contriever: Towards Unsupervised Dense Information Retrieval with Contrastive Learning This repository contains pre-trained models and some evaluation

Meta Research 207 Jan 08, 2023
TensorFlow2 Classification Model Zoo playing with TensorFlow2 on the CIFAR-10 dataset.

Training CIFAR-10 with TensorFlow2(TF2) TensorFlow2 Classification Model Zoo. I'm playing with TensorFlow2 on the CIFAR-10 dataset. Architectures LeNe

Chia-Hung Yuan 16 Sep 27, 2022
PClean: A Domain-Specific Probabilistic Programming Language for Bayesian Data Cleaning

PClean: A Domain-Specific Probabilistic Programming Language for Bayesian Data Cleaning Warning: This is a rapidly evolving research prototype.

MIT Probabilistic Computing Project 190 Dec 27, 2022
Does Oversizing Improve Prosumer Profitability in a Flexibility Market? - A Sensitivity Analysis using PV-battery System

Does Oversizing Improve Prosumer Profitability in a Flexibility Market? - A Sensitivity Analysis using PV-battery System The possibilities to involve

Babu Kumaran Nalini 0 Nov 19, 2021
(NeurIPS '21 Spotlight) IQ-Learn: Inverse Q-Learning for Imitation

Inverse Q-Learning (IQ-Learn) Official code base for IQ-Learn: Inverse soft-Q Learning for Imitation, NeurIPS '21 Spotlight IQ-Learn is an easy-to-use

Divyansh Garg 102 Dec 20, 2022
CNN Based Meta-Learning for Noisy Image Classification and Template Matching

CNN Based Meta-Learning for Noisy Image Classification and Template Matching Introduction This master thesis used a few-shot meta learning approach to

Kumar Manas 2 Dec 09, 2021
This is a computer vision based implementation of the popular childhood game 'Hand Cricket/Odd or Even' in python

Hand Cricket Table of Content Overview Installation Game rules Project Details Future scope Overview This is a computer vision based implementation of

Abhinav R Nayak 6 Jan 12, 2022
One-Shot Neural Ensemble Architecture Search by Diversity-Guided Search Space Shrinking

One-Shot Neural Ensemble Architecture Search by Diversity-Guided Search Space Shrinking This is an official implementation for NEAS presented in CVPR

Multimedia Research 19 Sep 08, 2022
PyTorch code for 'Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale Learning'

Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale Learning This repository is for EMSRDPN introduced in the foll

7 Feb 10, 2022